Multiwavelength Approach to joint formation and evolution of Galaxies and AGNs

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Multi-wavelength Approachto joint formation and
evolution of Galaxies and AGNs
Fabio Fontanot Max-Planck-Institute fuer
Astronomie, Heidelberg Lubiana, 25/03/08
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Outline

• Introduction to the problem of
  joint Formation of Galaxies and AGNs
• Theoretical perspective
• Observational Constraints
• Original Results
• Assembly of Massive Galaxies
• Evolution of the AGN population

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Galaxy Formation and Evolution
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1. Baryonic gas falls in the gravitational
potential of Dark Matter Halos
2. Baryonic gas is shock-heated to the virial
temperature
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TIME
Dark Matter Halos Merger Tree
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5. Interaction of galaxies with the enviroment
instabilities modify galactic structures (bulge
formation)
Tidal Stripping Dynamical Friction
Merging
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Galactic Winds
Stellar
6. Thermal processes in the baryonic gas
Feedback
Infall
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Active Galactic Nuclei
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AGNs Quasars

• Compact and luminous sources (L1046-49erg/s)
• Accretion of gas onto a Supermassive Black Hole
  (106-9 Msun) at the center of galaxies
• Strong Connection with host galaxy formation
  and evolution (feedback, energy transfer)

Padovani Urry 1995
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AGN Host Galaxy connection
Marconi Hunt 2004
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Observational Constraints
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Downsizing

• Archeological
• Stellar populations in in massive galaxies are
  older than those in low-mass galaxies
• Massive galaxies are more metal rich than
  low-mass counterparts (Gallazzi 2005)
• Star formation timescales are shorter in massive
  galaxies (Thomas 2005)
• Stellar Mass Assembly
• Massive galaxies already in place at high-z
  (Cimatti 2006, Conselice 2007)
• Star Formation Activity
• Specific star formation rate declines more
  rapidly for massive galaxies (Panther 2007
  Zheng 2007)

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• Space density of brighter AGNs peaks at higher
  redshift with respect to fainter ones

• Most massive BH accreted their mass faster and at
  higher redshift with respect to low-mass ones
  (Shankar 2004)
• Anti-hierarchical behavior of baryons

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Zheng et al. 2007
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MORGANA Model for the Rise of GAlaxies aNd
Agns(Monaco, Fontanot Taffoni, 2007)
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1 Complexity

• Mass flows
• Outside the integration
• disc instabilities
• minor and major mergers
• tidal stripping and disruption
• quasar winds

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2 Cooling Infall
equilibrium computed at each time-step
hot polytropic gas in hydrostatic equilibrium
the cooling radius is a dynamical variable that
takes into account the hot gas from feedback
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Viola 2008
MORGANA Cooling

• Simulations
• Gadget2 SPH code with entropy-conserving
  integration
• 60000 DM particles and 60000 gas particles inside
  the virial radius
• Static DM halo with NFW profile
• Gas profile in hydrostatic equilibrium
• Radiative cooling switched on

Classical Cooling
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3 Feedback

• Stellar Feedback
• Stars provide both thermal and kinetic energy to
  cold gas (by Starlight and/or SNe explosions)
• Improved modeling (Monaco, 2004) with two phase
  treatment of star forming ISM

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3 Feedback

• Stellar Feedback
• Stars provide both thermal and kinetic energy to
  cold gas (by Starlight and/or SNe explosions)
• Improved modeling (Monaco, 2004) with two phase
  treatment of star forming ISM
• Kinetic feedback
• Velocity dispersion of cold clouds
• scold s0 t-?

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3 Feedback

• QSO feedback
• Accretion on central BH
• Energy Input

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3 Feedback

• QSO feedback
• Accretion on central BH
• Energy Input
• QSO shining is able to change the physical
  conditions of stellar feedback in galaxies
  (Monaco Fontanot, 2005)
• Triggering of galactic winds (QSO Mode?)

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3 Feedback

• QSO feedback
• Accretion on central BH
• Energy Input
• QSO shining is able to change the physical
  conditions of stellar feedback in galaxies
  (Monaco Fontanot, 2005)
• Triggering of galactic winds (QSO Mode?)
• Feedback from Radio Jets
• Bringing energy from the center to the external
  regions
• Quenching of the cooling flows (Radio Mode)

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4 Diffuse Stellar Component
Monaco, Murante, Borgani, Fontanot, 2006
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Cosmic Star Formation Rate
Hopkins 2004
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Stellar Mass Function
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Fontana 2006
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The effect of stellar feedbackand quasar
windson the AGN population(Fontanot, Monaco,
Cristiani Tozzi 2006)
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Hard X-ray and Optical LF
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Space Density Evolution
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Effect of Kinetic Feedback
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Black Hole Bulge Relation
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Evolution of the Black Hole Bulge Relation
Peng 2006
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The assembly of massive galaxies in hierarchical
cosmology (Fontanot, Monaco, Silva Grazian 2007)
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Spectrophotometric Codes

• GRASIL (Silva 1998)
• Includes the effect of age-selective extinction
  (younger stellar populations are more affected by
  dust extinction)
• Computes dust emission in infrared regions
• Salpeter IMF

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Redshift Distribution
Cimatti 2002
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Redshift Distribution
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K-band LFs
Pozzetti 2003
Cirasuolo 2006
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SCUBA counts
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Downsizing?
MORGANA Predictions
GOODS-MUSIC data
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Conclusions

• Models based on Lambda CDM cosmology are able to
  reproduce the properties of AGN and massive
  galaxies
• We are able to reproduce the anti-hierarchical
  behavior of black hole growth
• Winds are needed
• Kinetic stellar feedback
• We are able to reproduce the early assembly and
  late almost-passive evolution of massive galaxies
  
• Stellar feedback
• Improved modeling of cooling
• We are not able to reproduce the observed
  downsizing trend of stellar mass assembly

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Metallicity
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The High-z QSOLuminosity Function(Fontanot et
al., 2007)
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Motivations

• Quasars are luminous but rare sources
• Large area surveys vs Deep survey
• Bright end vs Faint end
• Faint end of Luminosity Function
• Measure QSO contribution to the UV background
  (Madau et al., 1999)
• Constraints on the mechanisms responsible of the
  joint formation of supermassive black holes and
  host galaxies

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GOODS Project

• Study Galaxy Formation and Evolution over a wide
  range of cosmic lookback times (Giavalisco et
  al., 2004)
• Multiwavelenght survey
• Two fields centered on HDFN and CDFS
• total area 320 sqarcmin

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Selection of optical candidates

• Optical data from ACS (B435, V606, i775, z850)
• Selection Criteria (Cristiani et al., 2004)
• Magnitude Limit 22.45 lt z850 lt 25.25
• Color Criteria tested on template spectra
  (Cristiani Vio, 1990)
• (i-zlt0.35)n(V-ilt1.00)n(1.00ltB-Vlt3.00)
• (i-zlt0.35)n(B-Vgt3.00)
• (i-zlt0.50)n(V-igt0.80)n(B-Vgt2.00)
• (i-zlt1.00)n(V-igt1.90)

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Selection of optical candidates

• Quasar selected with 3.5ltzlt5.2
• Also included Ly-break and Seyfert Galaxies

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Matching with X-ray observations

• 1202 optically selected candidates
• 557 in HDFN 645 in CDFS
• Match with Chandra surveys
• Alexander et al., 2003
• Giacconi et al., 2002
• 16 Final candidates
• 10 in HDFN 6 in CDFS

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X-ray Matching

• Estimate of Visibility (Vignali et al. 2003)
• Any z gt 3.5 x-ray source must harbour an AGN
• Type I QSOs with M145 lt -21 up to z 5.2

GOODS -S 6
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Spectroscopic Follow-up

• 50 LBGs out of optically selected candidates
• Results QSO candidates (Vanzella et al., 2004)
• 3 low-z galaxies
• 12 QSOs with 2.6 lt z lt 5.2
• 2 QSOs with z gt 4
• QSO at z 5.186 (Barger et al. 2001)
• QSO at z 4.76 (Vanzella et al. 2004)

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High-z LF

• Faint QSOs
• GOODS observations (Cristiani et al., 2004)
• Bright QSOs
• SDSS Quasar Data Release 3 (DR3QSO
  Schneider et al. 2005)
• Key Issues
• Understanding systematics, selection effects and
  completeness
• Reproducing survey features

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Predicting QSO color evolution

• Define a Statistical Sample of QSOs
• High completeness redshift interval
• 2.2 lt z lt 2.25
• High quality QSO spectra from SDSS
• Sample of 215 QSOs
• Building up template library
• Computing restframe spectra
• Fitting continuum
• Simulating high redshift objects
• Computing Statistical Properties

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Choosing Redshift Interval
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Results Color Diagrams
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Results Color Evolution
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Computing LFs

• Analytical form for LF
• Compute expected number of QSOs
• Simulate magnitudes in photometric systems
• Mock SDSS and GOODS catalogues
• Apply selection criteria
• Mock SDSS and GOODS selected catalogues
• Compare observed and simulated objects
• Define chi square estimator
• Evaluate agreement between data and LF

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Results LFs
BRIGHT END
FAINT END
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Results
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Completeness